Obstacle detection and vehicle navigation using resolution-adaptive fusion of point clouds

ABSTRACT

A method for obstacle detection and navigation of a vehicle using resolution-adaptive fusion includes performing, by a processor, a resolution-adaptive fusion of at least a first three-dimensional (3D) point cloud and a second 3D point cloud to generate a fused, denoised, and resolution-optimized 3D point cloud that represents an environment associated with the vehicle. The first 3D point cloud is generated by a first-type 3D scanning sensor, and the second 3D point cloud is generated by a second-type 3D scanning sensor. The second-type 3D scanning sensor includes a different resolution in each of a plurality of different measurement dimensions relative to the first-type 3D scanning sensor. The method also includes detecting obstacles and navigating the vehicle using the fused, denoised, and resolution-optimized 3D point cloud.

FIELD

The present disclosure relates to obstacle detection and vehiclenavigation and more particularly to obstacle detection and vehiclenavigation using resolution-adaptive fusion of point clouds.

BACKGROUND

Autonomous vehicles may use three-dimensional (3D) scanning devices,such as Light Detection and Ranging (LIDAR) sensors, vision sensors,radar, or other 3D sensing devices for use in obstacle detection andvehicle navigation. Such sensors are capable of generating point cloudswhich can be combined using a sensor fusion method or process. Someexisting sensor fusion methods process raw point cloud data from eachsensor separately and independently, and then perform fusion bycombining object tracks from the different sensors. Other sensor fusionmethods perform fusion at the point cloud level but only by registeringand aligning the point clouds in a common coordinate system and thenfitting a mesh or surface to the composite point clouds. These existingpoint cloud fusion methods do not consider resolution differences ofsensors and do not compensate for poor resolution of a particular typesensor in one or more dimensions. Accordingly, there is a need forprocessing point clouds from different-types of sensors that takes intoconsideration sensor resolution and automatically compensates for asensor's poor resolution to detect obstacles for navigation of avehicle.

SUMMARY

In accordance with an embodiment, a method for obstacle detection andnavigation of a vehicle using resolution-adaptive fusion includesperforming, by a processor, a resolution-adaptive fusion of at least afirst three-dimensional (3D) point cloud and a second 3D point cloud togenerate a fused, denoised, and resolution-optimized 3D point cloud thatrepresents an environment associated with the vehicle. The first 3Dpoint cloud is generated by a first-type 3D scanning sensor and thesecond 3D point cloud is generated by a second-type 3D scanning sensor.The second-type 3D scanning sensor includes a different resolution ineach of a plurality of different measurement dimensions relative to thefirst-type 3D scanning sensor. The method also includes detectingobstacles and navigating the vehicle using the fused, denoised, andresolution-optimized 3D point cloud.

In accordance with an embodiment, a system for obstacle detection andnavigation of a vehicle using resolution-adaptive fusion includes aprocessor and a memory associated with the processor. The memoryincludes computer-readable program instructions that, when executed bythe processor, causes the processor to perform a set of functions. Theset of functions include performing a resolution-adaptive fusion of atleast a first three-dimensional (3D) point cloud and a second 3D pointcloud to generate a fused, denoised, and resolution-optimized 3D pointcloud that represents an environment associated with the vehicle. Thefirst 3D point cloud is generated by a first-type 3D scanning sensor andthe second 3D point cloud is generated by a second-type 3D scanningsensor. The second-type 3D scanning sensor includes a differentresolution in each of a plurality of different measurement dimensionsrelative to the first-type 3D scanning sensor. The set of functions alsoincludes detecting obstacles and navigating the vehicle using the fused,denoised, and resolution-optimized 3D point cloud.

In accordance with an embodiment and any of the preceding embodiment,wherein performing the resolution-adaptive fusion includes generating afirst volumetric surface function by performing a 3D convolution of eachmeasured point from a plurality of measured points of the first 3D pointcloud with an associated 3D point spread function of the first-type 3Dscanning sensor for representing an uncertainty in a spatial location ofeach measured point. The first volumetric surface function incorporatesa resolution of the first-type 3D scanning sensor. Performing theresolution-adaptive fusion also includes generating a second volumetricsurface function by performing a 3D convolution of each measured pointfrom a plurality of measured points of the second 3D point cloud with anassociated 3D points spread function of the second-type 3D scanningsensor for representing an uncertainty in a spatial location of eachmeasured point. The second volumetric surface function incorporates aresolution of the second-type 3D scanning sensor.

In accordance with an embodiment and any of the preceding embodiments,wherein performing the resolution-adaptive fusion further includesforming a 3D composite volumetric surface function by multiplying oradding the first volumetric surface function for the first-type 3Dscanning sensor and the second volumetric surface function for thesecond-type 3D scanning sensor. Inaccurate point cloud data from onetype 3D scanning sensor will be compensated by accurate point cloud datafrom the other type scanning sensor by forming the 3D compositevolumetric surface function.

In accordance with an embodiment and any of the preceding embodiments,wherein forming the 3D composite volumetric surface function includesadding the first volumetric surface function and the second volumetricsurface function in response to a condition that causes one of the 3Dscanning sensors to be ineffective.

In accordance with an embodiment and any of the preceding embodiments,wherein forming the 3D composite volumetric surface function includesmultiplying the first volumetric surface function and the secondvolumetric surface function to enhance a resolution for detecting anobstacle in the environment associated with the vehicle compared tousing a volumetric surface function of only one of the 3D scanningsensors.

In accordance with an embodiment and any of the preceding embodiments,wherein performing the resolution-adaptive fusion further includesgenerating an iso-contour of the 3D composite volumetric surfacefunction by performing automated edge-based thresholding to find a bestresolution-adaptive iso-contour of the 3D composite volumetric surfacefunction. The automated edge-based thresholding is based on volumetricsurface function edge map optimization.

In accordance with an embodiment and any of the preceding embodiments,wherein performing the automated edge-based thresholding includesincrementing a threshold value over a preset range of values todetermine the threshold value that maximizes a number of edges in atwo-dimensional (2D) edge map of the iso-contour of the 3D compositevolumetric surface function.

In accordance with an embodiment and any of the preceding embodiments,wherein performing the resolution-adaptive fusion further includesresampling the iso-contour of the 3D composite volumetric surfacefunction on a uniform grid to form the fused, denoised, andresolution-optimized 3D point cloud.

In accordance with an embodiment and any of the preceding embodiments,wherein the method and set of function of the system further includepresenting a representation of the environment associated with thevehicle using the fused, denoised, and resolution-optimized 3D pointcloud.

In accordance with an embodiment and any of the preceding embodiments,wherein the method and set of functions of the system further includeusing the fused, denoised, and resolution-optimized 3D point cloud todetect and avoid the obstacles by the vehicle.

In accordance with an embodiment and any of the preceding embodiments,wherein the method and set of functions of the system further includegenerating a sequence of fused, denoised, and resolution-optimized 3Dpoint clouds and tracking a moving obstacle from among the obstaclesusing the sequence of fused, denoised, and resolution-optimized 3D pointclouds.

In accordance with an embodiment and any of the preceding embodiments,wherein the first-type 3D scanning sensor comprises one of a radar, astereo vision sensor, a monocular vision sensor, or a LIDAR sensor, andwherein the second-type 3D scanning sensor comprises a different-typesensor from the first-type 3D scanning sensor.

The features, functions, and advantages that have been discussed can beachieved independently in various embodiments or may be combined in yetother embodiments, further details of which can be seen with referenceto the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block schematic diagram of an example of a vehicleincluding a system for obstacle detection and navigation of the vehicleusing resolution-adaptive fusion of point clouds in accordance with anembodiment of the present disclosure.

FIG. 1B is a block schematic diagram of an example of a vehicleincluding a system for obstacle detection and navigation of the vehicleusing resolution-adaptive fusion of point clouds in accordance withanother embodiment of the present disclosure.

FIGS. 2A-2B are a flow chart of an example of a method for obstacledetection and navigation of a vehicle using resolution-adaptive fusionof point clouds in accordance with an embodiment of the presentdisclosure.

FIGS. 3A-3B are an illustration of the resolution-adaptive fusionprocessing flow of FIGS. 2A-2B.

FIGS. 4A and 4B are each an illustration of an example of measuredpoints of different 3D point clouds and an associated resolution ormeasurement uncertainty of each measured point.

FIG. 5 is an illustration of forming a volumetric surface functionincorporating sensor resolutions by convolution of measured points of a3D point cloud with the associated point spread function (PSF) for aparticular type 3D scanning sensor that generated the 3D point cloud inaccordance with an embodiment of the present disclosure.

FIG. 6 is a flow chart of an example of a method for performingautomated edge-based thresholding to find a best resolution-adaptiveiso-contour of the 3D composite volumetric surface function (VSF) inaccordance with an embodiment of the present disclosure.

FIG. 7 is an illustration of the automated edge-based thresholding ofFIG. 6 to find the best resolution-adaptive iso-contour of the 3Dcomposite VSF.

FIG. 8 is an illustration of an example of a graph of variation of atotal length of edges in a 2D projection edge map of an iso-contour of aVSF versus threshold value in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

The following detailed description of embodiments refers to theaccompanying drawings, which illustrate specific embodiments of thedisclosure. Other embodiments having different structures and operationsdo not depart from the scope of the present disclosure. Like referencenumerals may refer to the same element or component in the differentdrawings.

The present disclosure may be a system, a method, and/or a computerprogram product. The computer program product may include acomputer-readable storage medium (or media) having computer-readableprogram instructions thereon for causing a processor to carry outaspects of the present disclosure.

The computer-readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer-readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer-readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer-readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer-readable program instructions described herein can bedownloaded to respective computing/processing devices from acomputer-readable storage medium or to an external computer or externalstorage device via a network, for example, the Internet, a local areanetwork, a wide area network and/or a wireless network. The network maycomprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer-readable programinstructions from the network and forwards the computer-readable programinstructions for storage in a computer-readable storage medium withinthe respective computing/processing device.

Computer-readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. Thecomputer-readable program instructions may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider). In some embodiments, electronic circuitry including, forexample, programmable logic circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) may execute thecomputer-readable program instructions by utilizing state information ofthe computer-readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer-readable program instructions.

These computer-readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer-readable program instructionsmay also be stored in a computer-readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that thecomputer-readable storage medium having instructions stored thereincomprises an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock diagram block or blocks.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIG. 1A is a block schematic diagram of an example of a vehicle 100including a system 102 for obstacle 104 a or 104 b detection andnavigation of the vehicle 100 using resolution-adaptive fusion 138 of 3Dpoint clouds 106 and 108 in accordance with an embodiment of the presentdisclosure. An example of a method 200 for performingresolution-adaptive fusion 138 will be described with reference to FIGS.2A and 2B. In accordance with the example in FIG. 1A, the obstacle 104includes one or more stationary obstacles 104 a and one or more movingobstacles 104 b in an environment 110 associated with the vehicle 100.In accordance with different examples, the vehicle 100 is an aircraft,an unmanned aerial vehicle (UAV), or other type autonomous vehicle.

The system 102 includes a first-type 3D scanning sensor 112 and at leasta second-type 3D scanning sensor 114. In other embodiments, the system102 includes more than two types of 3D scanning sensors 112 and 114.Examples of the types of 3D scanning sensors include but are notnecessarily limited to any type of vision sensor, such as a stereovision sensor or a monocular vision sensor, a Light Detection andRanging (LIDAR) sensor, radar and any other type 3D sensing system thatmeasures and tracks objects in large spatial volumes. Eachdifferent-type 3D scanning sensor 112 and 114 respectively collects oneor more electronic images 118 and 120 of the environment 110 associatedwith the vehicle 100. As described in more detail herein, the electronicimages 118 and 120 have different resolutions for different directionsof view and distances from the 3D scanning sensor 112 or 114 based onthe sensor resolution of the particular type 3D scanning sensor 112 and114 for different directions and distances. In the example in FIG. 1A,the environment 110 includes a ground plane 122, one or more stationaryobstacles 104 a and one or more moving obstacles 104 b.

Each electronic image 118 includes a 3D point cloud 106 and eachelectronic image 120 includes a 3D point cloud 108. Each 3D point cloud106 and 108 includes a multiplicity of measured points 124 and eachmeasured point 124 includes point cloud data 126. Each measured point124 correspond to a point 128 on a surface 130 of one of the obstacles104 a and 104 b in the environment 110 associated with the vehicle 100.The point cloud data 126 includes position or location information forthe corresponding point 128 on the surface 130 of the obstacle 104 a or104 b and measurement uncertainty as described in more detail withreference to FIGS. 4 and 5 .

The system 102 also includes a processor 132 and a memory 134 associatedwith the processor 132. In the example of FIG. 1A, the memory 134 isshown as part of the processor 132. In other embodiments, the memory 134is a separate component from the processor 132. The memory 134 includescomputer-readable program instructions 136 that, when executed by theprocessor 132, cause the processor 132 to perform a set of functions137. The set of functions 137 define the resolution-adaptive fusion 138process. As described in more detail with reference to FIGS. 2A-2B,resolution-adaptive fusion 138 combines 3D point clouds, such as pointclouds 106 and 108, from multiple types of 3D scanning sensors, e.g., 3Dscanning sensors 112 and 114, with different resolutions in differentdimensions or directions into a single uniformly sampled point cloud,for example, fused, denoised, and resolution-optimized 3D point cloud140. The fused, denoised, and resolution-optimized 3D point cloud 140has resolution in each dimension approximately equal to the resolutionof a particular type 3D scanning sensor 112 or 114 that has the bestresolution for that dimension or in that direction. Resolution-adaptivefusion 138 uses models of sensor resolution for different directions anddistances to preferentially select the most accurate data in creatingthe fused, denoised, and resolution-optimized 3D point cloud 140. Asdescribed in more detail with reference to FIGS. 2A-2B,resolution-adaptive fusion 138 incorporates sensor resolution models tocompensate for the poor resolution of a 3D scanning sensor 112 or 114 inone dimension by automatically suppressing low resolution data andreplacing it with data from another 3D scanning sensor 112 or 114 thatperforms better in that dimension. This enables high performance sensingin all dimensions by using different-types of 3D scanning sensors 112and 114. For example, at least the first-type 3D scanning sensor 112 hasgood performance in one or more dimensions and at least the second-type3D scanning sensor 114 has good performance in the dimensions where thefirst-type scanning sensor 114 has poor performance. As a furtherexample, resolution-adaptive fusion 138 enables replacing LIDAR withvision and radar 3D scanning sensors since vision and radar havecomplementary performance capabilities in different dimensions andenvironmental conditions. Another advantage of resolution-adaptivefusion 138 is that all parameters can be either calculated from sensorresolution models or automatically determined by the system 102 whichenables resolution-adaptive fusion 138 to automatically adapt to theenvironment 110. As described in more detail with reference to FIGS. 2Aand 2B, the resolution-adaptive fusion 138 performs a 3D convolution ofeach measured point 124 of the first 3D point cloud 106 with anassociated 3D point spread function (PSF) 142 of the first-type 3Dscanning sensor 112 to incorporate the sensor resolution of thefirst-type 3D scanning sensor 112 in generating the fused, denoised, andresolution-optimized 3D point cloud 140. The resolution-adaptive fusion138 also performs a 3D convolution of each measured point 124 of thesecond 3D point cloud 108 with an associated 3D PSF 144 of thesecond-type scanning sensor 114 to incorporate the sensor resolution ofthe second-type 3D scanning sensor 114 in generating the fused,denoised, and resolution-optimized 3D point cloud 140. The sensorresolution models are represented by the 3D PSFs 142 and 144. The PSFs142 and 144 can be spatially varying. For example, the range ofresolution of a stereo vision sensor depends on the range of each 3Dmeasured point 124 from the sensor.

In accordance with different exemplary embodiments, the vehicle 100includes at least one of a perception system 146 and a display 148, avehicle control system 150, and a moving obstacle tracking system 152.The perception system 146 is configured to use the fused, denoised, andresolution-optimized 3D point cloud 140 to detect and avoid obstacles104 a and 104 b and for navigation of the vehicle 100 in the environment110. For an example where the vehicle 100 is an aircraft, the fused,denoised, and resolution-optimized 3D point cloud 140 is presented on adisplay 148 to provide enhanced situational awareness to a flight crewof the obstacles 104 a and 104 b, for example, under adverse conditions,such as low visibility. In an example where the vehicle 100 is anunmanned aerial vehicle or autonomous vehicle, the perception system 146uses the fused, denoised, and resolution-optimized 3D point cloud 140 todetect and avoid obstacles 104 a and 104 b.

The vehicle control system 150 is configured to use the fused, denoised,and resolution-optimized 3D point cloud 140 for navigation of thevehicle 100 and to avoid the obstacles 104 a and 104 b.

The moving obstacle tracking system 152 is configured to use a sequenceof fused, denoised, and resolution-optimized 3D point clouds 140 totrack one or more moving obstacles 104 b. An example of generating asequence of fused, denoised, and resolution-optimized 3D point clouds140 for tracking moving obstacles 104 b will be described in more detailwith reference to FIGS. 2A-2B.

FIG. 1B is a block schematic diagram of an example of a vehicle 101including a system 102 for obstacle 104 a and 104 b detection andnavigation of the vehicle 101 using resolution-adaptive fusion 138 ofpoint clouds 106 and 108 in accordance with another embodiment of thepresent disclosure. The vehicle 101 is similar to the vehicle 100 inFIG. 1A except at least one of the perception system 146 and associateddisplay 148, vehicle control system 150 and moving obstacle trackingsystem 152 are located at a ground station 154 or a remote station. Inaccordance with an example, the vehicle 101 is an autonomous vehicle,such as an unmanned aerial vehicle. The vehicle 101 includes atransceiver 156 for transmitting the fused, denoised, andresolution-optimized 3D point cloud 140 to another transceiver 158 atthe ground station 154 for use by the perception system 146, vehiclecontrol system 150, and moving obstacle tracking system 152 similar tothat previously described. The transceiver 156 is also configured toreceive control signals from the transceiver 158 of the ground station154 to control operation of the vehicle 101.

In accordance with another embodiment, the processor 132 and associatedmemory 134 are also located at the ground station 154. In thisembodiment, the transceiver 156 transmits the electronic images 118 and120 including the respective 3D points clouds 106 and 108 to thetransceiver 158 of the ground station 154. The resolution-adaptivefusion 138 is then performed by the ground station 154 and controlsignals are transmitted by the ground station 154 to the vehicle 101 fornavigation of the vehicle 101 and avoidance of the obstacles 104 a and104 b.

FIGS. 2A-2B are a flow chart of an example of a method 200 for obstacle104 a and 104 b (FIGS. 1A and 1B) detection and navigation of a vehicle,such as vehicle 100 or 101 in FIGS. 1A and 1B, using resolution-adaptivefusion 138 of 3D points clouds 106 and 108 from different-types of 3Dscanning sensors 112 and 114 (FIGS. 1A and 1B) in accordance with anembodiment of the present disclosure. In accordance with the example inFIG. 1A, at least some functions of the method 200 are embodied in thecomputer-readable program instructions 136 and resolution-adaptivefusion 138.

In block 202, a first scanning operation is performed using a first-type3D scanning sensor 112 (FIGS. 1A and 1B) to collect one or moreelectronic images 118 of an environment 110 associated with the vehicle100 or 101. In block 204, at least a second scanning operation isperformed using a second-type 3D scanning sensor 114 to collect one ormore electronic images 120. Each electronic image 118 from thefirst-type 3D scanning sensor 112 includes a first 3D point cloud 106and each electronic image 120 from the second-type scanning sensor 114includes a second 3D point cloud. As previously described, each 3D pointcloud 106 and 108 includes a multiplicity of measured points 124. Eachmeasured point 124 corresponds to a point 128 on a surface 130 of anobstacle 104 a or 104 b. Each measured point 124 includes point clouddata 126. The point cloud data 126 includes at least information thatdefines a location of the point 128 on the surface 130 of the obstacle104 a and 104 b.

In block 206, resolution-adaptive fusion 138 is performed on at leastthe first 3D point cloud 106 and the second 3D point cloud 108 togenerate the fused, denoised, and resolution-optimized 3D point cloud140. In the exemplary embodiment in FIGS. 2A and 2B, theresolution-adaptive fusion 138 in block 206 includes blocks 208-224. Thefused, denoised, and resolution-optimized 3D point cloud 140 representsan environment 110 associated with the vehicle 100 or 101. As previouslydescribed, the first 3D point cloud 106 is generated by the first-type3D scanning sensor 112 and the second 3D point cloud 108 is generated bythe second-type 3D scanning sensor 114. The second-type 3D scanningsensor 114 includes a different resolution in each of a plurality ofdifferent measurement dimensions relative to the first-type 3D scanningsensor 112. As previously discussed, the fused, denoised, andresolution-optimized 3D point cloud 140 is used to detect any obstacles104 a and 104 b and navigate the vehicle 100 or 101.

A typical combination of at least first-type 3D scanning sensors 112 andsecond-type 3D scanning sensors 114 include, but are not necessarilylimited to, vision sensors, radar, and LIDAR sensors. Each of thesedifferent-type 3D scanning sensors 112 and 114 have strengths, but alsoweaknesses that are compensated by one of the other type 3D scanningsensors 112 and 114. Since no one type 3D scanning sensor 112 or 114 hasan ideal set of capabilities, multiple different-types of 3D scanningsensors 112 and 114 are utilized and fused together to form a unifiedrepresentation of the environment 110 of the vehicle 100 or 101 forobstacle 104 a and 104 b detection and classification. For example,stereo or monocular vision 3D scanning sensors have good resolution inazimuth and elevation angles and range resolution that is good at shortranges, but they rapidly degrade for longer ranges. Vision 3D scanningsensors are also low cost and compact. LIDAR sensors offer excellentrange resolution and intermediate azimuth and elevation resolution, butthey are bulky and expensive. Both vision sensors and LIDAR sensors canbe compromised by weather conditions. Radar has good range resolution,but poor resolution in azimuth and elevation. Unlike the other twosensors, radar can also measure velocity in the direction toward theradar using Doppler frequency shifts and can operate in all weatherconditions. While the exemplary embodiments in FIGS. 1A and 1Billustrate at least a first-type 3D scanning sensor 112 and asecond-type 3D scanning sensor 114, other embodiments include more thantwo types of 3D scanning sensors with different resolutioncharacteristics, so that point cloud data 126 from the different-typesensors can be used to generate the fused, denoised, andresolution-optimized 3D point cloud 140 using the resolution-adaptivefusion 138 process described herein.

Referring also to FIGS. 3A-3B, FIGS. 3A-3B are an illustration of theresolution-adaptive fusion 138 processing flow in block 206 of FIGS.2A-2B. In block 208 of FIG. 2A, a first volumetric surface function(VSF) 212 is generated by using a fast Fourier transform (FFT) in eachof the three spatial dimensions to generate the 3D convolution 302 (FIG.3A) of each measured point 124 from a plurality of measured points 124of the first 3D point cloud 106 with an associated 3D PSF 142 of thefirst-type 3D scanning sensor 112 to represent the uncertainty in thespatial location of each measured point 124. The first VSF 212incorporates a resolution of the first-type 3D scanning sensor 112.

In block 210, a second VSF 214 is generated by performing an FFT 3Dconvolution 304 (FIG. 3A) of each measured point 124 from a plurality ofmeasured points 124 of the second 3D point cloud 108 with an associated3D PSF 144 of the second-type 3D scanning sensor 114 to represent theuncertainty in the spatial location of each measured point 124 of thesecond 3D point cloud 108. The second VSF 214 incorporates a resolutionof the second-type 3D scanning sensor 114.

Resolution-adaptive fusion 138 assumes sensor resolution models areavailable for the resolution of each of the 3D scanning sensors 112 and114 in different viewing directions and distances. These resolutionmodels can be determined analytically from sensor physical models orempirically from measurements of resolution targets at several positionsthat are then interpolated to cover the entire measurement volume.Values of resolution or measurement uncertainty are associated with eachmeasured point 124 in the 3D point cloud 106 and 108 as illustrated inFIG. 4 . FIG. 4 is an illustration of an example of measured points 124of different 3D point clouds 106 and 108 and an associated resolution ormeasurement uncertainty 402 of each measured point 124. Each measuredpoint 124 is defined by its position and its position-dependentmeasurement uncertainty 3D point spread function (PSF) 142 or 144 whichis formed from a product of Gaussians for each dimension. The PSF axescorrespond to the resolutions or standard deviations σ_(x), σ_(y), andσ_(z) (FIG. 5 ) in range and two transverse directions of the measuredpoint 128 on the obstacle 104 a or 104 b as seen from the 3D scanningsensor 112 or 114 that measured the point 128. The orientation of thePSF 142 and 144 is represented by two angles, azimuth (θ) and elevation(ϕ). A total of eight parameter values are associated with each measuredpoint 124 to represent both the position and measurement uncertainty ofthe point 124 in any direction and for any distance. The PSF 142 or 144can potentially vary with location in the measurement volume dependingon the sensor characteristics.

Since the 3D scanning sensors 112 and 114 have different resolutions indifferent directions and distances, the resolution-adaptive fusion 138is configured to preferentially utilize the point cloud data 126 withthe best resolution from each 3D scanning sensor 112 and 114. The basicprinciple of the resolution-adaptive fusion 138 is illustrated in FIG. 5. Resolution-adaptive fusion 138 uses an intermediate “implicit”volumetric representation 502 of the evidence for object surfaces in aspatial volume to generate a new higher accuracy point cloud 504 withreduced noise that is sampled on a uniform spatial grid. Unlike“explicit” representations of surfaces, which are lists of points on thesurface with their 3D coordinates, implicit methods represent a surfacein terms of functions defined in the 3D space. For example, one possibleimplicit representation is a 3D function whose value at each 3D voxel isgiven by the density of points at that location. Another implicitrepresentation, which resolution-adaptive fusion 138 utilizes, is todefine a surface as an iso-contour or level set of a 3D function thatmodels the resolutions of the 3D scanning sensors 112 and 114 atdifferent locations and in different directions. In resolution-adaptivefusion 138, the 3D function is the Volumetric Surface Functions (VSFs)212 and 214 formed by convolving the measured points 124 measured byeach 3D scanning sensor 112 and 114 with the associated 3D PSF 142 and144 of each 3D scanning sensor 112 and 114. The PSF 142 and 144represents the uncertainty in position of each measured point 124 in alldirections and can be modeled as a product of Gaussians with position,shape, and orientation of the PSF 142 and 144 reflecting the resolutionproperties of the particular 3D scanning sensor 112 and 114. The PSFs142 and 144 are derived from a priori sensor models or measuredempirically from data. The 3D convolution 302 and 304 replaces eachmeasured point 124 with the associated sensor's 3D PSF 142 or 144 forthat location, as shown in FIG. 5 , and are efficiently implementedusing fast Fourier transforms.

The VSFs 212 and 214 are each a 3D voxel-based 3D volumetricrepresentation of the 3D point cloud 106 or 108 that is a measure of theevidence for a surface 130 of an obstacle 104 a or 104 b at every 3Dspatial location which incorporates the resolutions of the corresponding3D scanning sensors 112 and 114. The VSFs 212 and 214 are analogous tohow a computed axial tomography (CAT) scan is a 3D function that mapsbody tissue density. As more points 124 are measured, the most likelysurface locations will correspond to voxels with the highest values ofVSF due to overlapping of PSFs from multiple measured points 124.

In block 216, a 3D composite VSF 218 is formed by multiplying or addingthe first VSF 212 for the first-type 3D scanning sensor 112 (FIGS. 1Aand 1B) and the second VSF 214 for the second-type 3D scanning sensor114 (FIGS. 1A and 1B). Inaccurate point cloud data 126 from one type 3Dscanning sensor 112 or 114 will be compensated by accurate point clouddata 126 from the other type 3D scanning sensor 112 or 114 by formingthe 3D composite VSF 218. The 3D composite VSF 218 is formed by addingthe first VSF 212 and the second VSF 214 in response to a condition thatcauses one of the 3D scanning sensors 112 or 114 to be ineffective. The3D composite VSF 218 is formed by multiplying the first VSF 212 and thesecond VSF 214 to enhance a resolution for detecting an obstacle 104 aor 104 b in the environment 110 associated with the vehicle 100 or 101compared to using a VSF 212 or 214 of only one of the 3D scanningsensors 112 or 114. Multiplication of the VSFs 212 and 214 isappropriate when all obstacles in the environment are detected by two ormore 3D scanning sensors 112 and 114 since the composite resolution ineach dimension will be that of the higher-resolution 3D scanning sensor112 or 114 for that dimension. However, if an obstacle 104 a or 104 b isdetected by only one 3D scanning sensor 112 or 114, then the VSF 212 or214 of the other sensor 112 or 114 won't cover that obstacle 104 a and104 b and the product of the VSFs 212 and 214 will be zero for thatobstacle 104 a or 104 b. However, if the VSFs 212 and 214 are addedtogether, then obstacles 104 a or 104 b detected by only one 3D scanningsensor 112 or 114 will be represented in the 3D composite VSF 218. Theresolution of obstacles 104 a and 104 b detected by multiple 3D scanningsensors 112 and 114 may then not be improved as much as in themultiplicative case, although simulations show that the performanceimprovement is still high. The preferred operating mode may be tocalculate results for both multiplying and adding the VSFs 212 and 214,using the additive VSF to initially detect obstacles 104 a and 104 b,and then refine the resolution of the obstacles 104 a and 104 b usingthe multiplicative VSF.

In block 220 in FIG. 2B, an iso-contour 222 (see also FIG. 3B) of the 3Dcomposite VSF 218 is generated by performing automated edge-basedthresholding 306 (FIG. 3B) to find a best resolution-adaptiveiso-contour 222 of the 3D composite VSF 218. The automated edge-basedthresholding 306 is based on VSF edge map optimization. An example of amethod for automated edge-based thresholding will be described withreference to FIGS. 6 and 7 . As described in more detail with referenceto FIGS. 7 and 8 , performing the automated edge-based thresholding 306includes increasing a threshold value 702 a-702 c over a preset range ofvalues to determine the threshold value (702 b in the example in FIG. 7) that maximizes a number of edges in a two-dimensional (2D) edge map704 b of the iso-contour 222 of the 3D composite VSF 218.

In block 224, performing the resolution-adaptive fusion 138 furtherincludes resampling the iso-contour 222 of the 3D composite VSF 218 on auniform grid 308 (FIG. 3B) to form the fused, denoised, andresolution-optimized 3D point cloud 140. In accordance with an example,the resampling includes voxel-based point cloud resampling 310 (FIG. 3B)of the iso-contour 222 of the 3D composite VSF 218 to provide the fused,denoised, and resolution-optimized 3D point cloud 140. Voxel-based pointcloud resampling uses as inputs the point cloud points 124 measured bysensors 112 and 114 that are irregularly distributed in 3D space due tothe local topography of the sensed environment. The 3D composite VSF 218is a continuous function defined in 3D space that is generated byconvolving each measured point 124 in each point cloud 106 and 108 withthe associated sensor's local PSF 142 and 144. The 3D volume domain ofthe 3D composite VSF 218 can be subdivided into regularly spacedcontiguous voxels whose values are the local average of the 3D compositeVSF 218 over the voxel volume, thereby performing a spatial sampling ofthe 3D composite VSF 218. Thresholding the 3D composite VSF 218 thenresults in an iso-contour 222 surface of the 3D composite VSF 218 thatis sampled at the voxel locations. The voxels that are above thethreshold value can then be replaced by points centered on thoseabove-threshold voxels. These new points form a resampled point cloud ona regularly-spaced 3D grid which is a fused, denoised, andresolution-optimized point cloud 140 formed from the individual sensormeasured point clouds 106 and 108.

In accordance with an embodiment, the method 200 returns to blocks 202and 204 and the method 200 repeats as previously described. The method200 is repeated a predetermined number of times or continuously duringoperation of the vehicle 100 or 101 to generate a sequence of fused,denoised, and resolution-optimized 3D points clouds 140. In block 226,the sequence of fused, denoised, and resolution-optimized 3D pointclouds 140 are used to navigate the vehicle 100 or 101 and to detect andavoid stationary obstacles 104 a and moving obstacles 104 b in block226.

In block 228, a moving obstacle 104 b is tracked using the sequence offused, denoised, and resolution-optimized 3D points clouds 140. In block230, a representation of the environment 110 associated with the vehicle100 or 101 is presented on the display 148 using the fused, denoised,and resolution-optimized 3D point cloud 140 or sequence of fused,denoised, and resolution-optimized 3D point clouds 140.

FIG. 6 is a flow chart of an example of a method 600 for performingautomated edge-based thresholding 306 (FIG. 3B) to find a bestresolution-adaptive iso-contour 222 of the 3D composite VSF 218 inaccordance with an embodiment of the present disclosure. The automatededge-based thresholding 306 is based on volumetric surface function(VSF) edge map optimization 700 as performed by method 600 andillustrated in FIG. 7 . In block 602 of FIG. 6 , a threshold value isset to zero (0). In block 604, an iso-contour 222 of the 3D compositeVSF 218 is generated by setting all voxels with values above thethreshold value to 1, and 0 otherwise.

In block 606, the iso-contour 222 of the 3D composite VSF 218 isprojected in a Z-direction to form a two-dimensional (2D) image 706(FIG. 7 ) on an X-Y plane.

In block 608, 2D edge detection is performed and a binary map 704 a-704c of the edges is formed. The binary map 704 a-704 c can be consideredto be an image where pixels are valued 1 at an edge location and 0everywhere else. Summing all the pixels in the binary map image istherefore a measure of the number of edge pixels.

In block 610, all pixels in the binary edge map 704 a-704 c are summedto measure the quantity of edges. The value of the sum of all edges issaved.

In block 612, a determination is made if the threshold value is equal toone (1). If the threshold value is not equal one (1), the value of thethreshold is incremented by a preset amount less than one (1) and themethod 600 returns to block 604. The method 600 then proceeds aspreviously described.

If the threshold value in block 612 is equal to one (1), the method 600advances to block 616. In block 616, the threshold is set to the valuethat maximizes the number of edges. In block 618, the set thresholdvalue is used to generate the optimum iso-contour 222 of the 3Dcomposite VSF 218.

Referring also to FIG. 7 , FIG. 7 is an illustration of the automatededge-based thresholding 306 of FIG. 6 to find the bestresolution-adaptive iso-contour 222 of the 3D composite VSF 218. Asillustrated in FIG. 7 , as the threshold value 702 a-702 c increasesfrom a small value, i.e., 0.1 to larger threshold values, the 2Dprojection (block 606 in FIG. 6 ) of the iso-contour 222 on the X-Yplane consists of a single large 3D blob 708 a that then separates intosmaller 3D blobs 708 b-708 c centered on each obstacle 104 a or 104 b asthe threshold value 702 a-702 c is incremented (block 614). These blobs708 a-708 c then continue to shrink until a certain threshold value, forexample 702 b, is reached where the blobs 708 b break up into 3D“islands” centered on each measure point 124. This threshold value 702 bis optimal in the sense that it is the largest threshold value thatminimizes the volume of the iso-contour 222 (and hence improves theresolution) while still maintaining full coverage of the obstacle 104 aor 104 b. As the threshold value 702 a-702 c is increased beyond thispoint, the blobs 708 a-708 c will shrink until they practicallydisappear (blob 708 c). A simple calculated variable that can be used todetect the optimal threshold value is the total length of edges in anedge map 706 of the projection of the iso-contour 222 on a 2D plane orthe X-Y plane as illustrated in FIG. 7 . By increasing the thresholdvalue 702 a-702 c over a range of values and selecting the thresholdvalue that maximizes the total 3D composite VSF iso-contour 222 edgelength, as shown in FIGS. 7 and 8 , the threshold value 702 a-702 c canbe set automatically.

FIG. 8 is an illustration of an example of a graph 800 of variation of atotal length of edges in a 2D projection edge map 706 (FIG. 7 ) of aniso-contour 222 of a VSF versus threshold value in accordance with anembodiment of the present disclosure. As illustrated in FIG. 8 , theoptimum threshold value 702 b for combining sensor data is the thresholdvalue that maximized the total edge length.

While the exemplary method 200 in FIGS. 2A and 2B includes two scanningoperations to generate a first and second 3D point cloud 106 and 108, inaccordance with other embodiments, more than two scanning operations areperformed. In another embodiment, at least a third scanning operation isperformed by a third-type 3D scanning sensor to generate at least athird 3D point cloud. The resolution-adaptive fusion 138 is thenperformed using at least three 3D point clouds. Resolution-adaptivefusion 138 may be performed using any number of 3D point cloudsgenerated by different-types of 3D scanning sensors to generate thefused, denoised, and resolution-optimized 3D point cloud 140.

From the embodiments described herein, those skilled in art willrecognize that resolution-adaptive fusion 138 is applicable for anyplatform that utilizes multiple sensors to sense an environment in 3Dfor applications such as obstacle detection and navigation by taxiingaircraft, unmanned aerial vehicles, and other autonomous vehicles.Resolution-adaptive fusion 138 improves the 3D resolution of the sensorsystem by enabling one sensor to compensate for a second sensor's poorresolution in a particular measurement direction or the second sensor'sinability to function effectively in the current weather and/or lightingconditions. For example, resolution-adaptive fusion 138 is configurableto automatically switch between low-cost and compact vision and radarsensors for different ranges and azimuth/elevation angles to create ahigh resolution fused 3D point cloud using the best sensor for eachdimension. One potential application is to reduce or eliminate the needfor expensive, low-resolution, and LIDAR sensors using a combination ofradar and vision sensors.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of embodiments ofthe disclosure. As used herein, the singular forms “a,” “an,” and “the”are intended to include the plural forms as well, unless the contextclearly indicates otherwise. It will be further understood that theterms “include,” “includes,” “comprises,” and/or “comprising,” when usedin this specification, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present embodiments has been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to embodiments in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of embodiments.

Although specific embodiments have been illustrated and describedherein, those of ordinary skill in the art appreciate that anyarrangement which is calculated to achieve the same purpose may besubstituted for the specific embodiments shown and that the embodimentshave other applications in other environments. This application isintended to cover any adaptations or variations. The following claimsare in no way intended to limit the scope of embodiments of thedisclosure to the specific embodiments described herein.

What is claimed is:
 1. A method for obstacle detection and navigation ofa vehicle using resolution-adaptive fusion, the method comprising:performing, by a processor, a resolution-adaptive fusion of at least afirst three-dimensional (3D) point cloud and a second 3D point cloud togenerate a fused, denoised, and resolution-optimized 3D point cloud thatrepresents an environment associated with the vehicle, wherein the first3D point cloud is generated by a first-type 3D scanning sensor and thesecond 3D point cloud is generated by a second-type 3D scanning sensor,wherein the second-type 3D scanning sensor includes a differentresolution in each of a plurality of different measurement dimensionsrelative to the first-type 3D scanning sensor, and wherein saidperforming the resolution-adaptive fusion further comprises: generatinga first volumetric surface function that incorporates a resolution ofthe first-type 3D scanning sensor; generating a second volumetricsurface functions that incorporates a resolution of the second-type 3Dscanning sensor; forming a 3D composite multiplicative volumetricsurface function by multiplying the first volumetric surface functionfor the first-type 3D scanning sensor and the second volumetric surfacefunction for the second-type 3D scanning sensor; and forming a 3Dcomposite additive volumetric surface function by adding the firstvolumetric surface function and the second volumetric surface function,wherein the 3D composite additive volumetric surface function is used todetect the obstacles and the 3D composite multiplicative volumetricsurface function is used to refine a resolution of the obstacles; andnavigating the vehicle using the fused, denoised, andresolution-optimized 3D point cloud.
 2. The method of claim 1, whereinsaid performing the resolution-adaptive fusion comprises: generating thefirst volumetric surface function by performing a 3D convolution of eachmeasured point from a plurality of measured points of the first 3D pointcloud with an associated 3D point spread function of the first-type 3Dscanning sensor for representing an uncertainty in a spatial location ofeach measured point, wherein the first volumetric surface functionincorporates a resolution of the first-type 3D scanning sensor; andgenerating the second volumetric surface function by performing a 3Dconvolution of each measured point from a plurality of measured pointsof the second 3D point cloud with an associated 3D points spreadfunction of the second-type 3D scanning sensor for representing anuncertainty in a spatial location of each measured point, wherein thesecond volumetric surface function incorporates a resolution of thesecond-type 3D scanning sensor.
 3. The method of claim 2, wherein saidperforming the resolution-adaptive fusion further comprises forming a 3Dcomposite volumetric surface function by multiplying or adding the firstvolumetric surface function for the first-type 3D scanning sensor andthe second volumetric surface function for the second-type 3D scanningsensor, wherein inaccurate point cloud data from one type 3D scanningsensor will be compensated by accurate point cloud data from the othertype scanning sensor by said forming the 3D composite volumetric surfacefunction.
 4. The method of claim 3, wherein said forming the 3Dcomposite volumetric surface function comprises adding the firstvolumetric surface function and the second volumetric surface functionin response to a condition that causes one of the 3D scanning sensors tobe ineffective.
 5. The method of claim 3, wherein said forming the 3Dcomposite volumetric surface function comprises multiplying the firstvolumetric surface function and the second volumetric surface functionto enhance a resolution for detecting an obstacle in the environmentassociated with the vehicle compared to using a volumetric surfacefunction of only one of the 3D scanning sensors.
 6. The method of claim3, wherein said performing the resolution-adaptive fusion furthercomprises generating an iso-contour of the 3D composite volumetricsurface function by performing automated edge-based thresholding to finda best resolution-adaptive iso-contour of the 3D composite volumetricsurface function, wherein the automated edge-based thresholding is basedon volumetric surface function edge map optimization.
 7. The method ofclaim 6, wherein said performing the automated edge-based thresholdingcomprises incrementing a threshold value over a preset range of valuesto determine the threshold value that maximizes a number of edges in atwo-dimensional (2D) edge map of the iso-contour of the 3D compositevolumetric surface function.
 8. The method of claim 6, wherein saidperforming the resolution-adaptive fusion further comprises resamplingthe iso-contour of the 3D composite volumetric surface function on auniform grid to form the fused, denoised, and resolution-optimized 3Dpoint cloud.
 9. The method of claim 1, further comprising presenting arepresentation of the environment associated with the vehicle using thefused, denoised, and resolution-optimized 3D point cloud.
 10. The methodof claim 9, further comprising using the fused, denoised, andresolution-optimized 3D point cloud to detect and avoid the obstacles bythe vehicle.
 11. The method of claim 1, wherein the first-type 3Dscanning sensor comprises one of a radar, a stereo vision sensor, amonocular vision sensor, or a LIDAR sensor, and wherein the second-type3D scanning sensor comprises a different-type sensor from the first-type3D scanning sensor.
 12. A system for obstacle detection and navigationof a vehicle using resolution-adaptive fusion, the system comprising: aprocessor; and a memory associated with the processor, wherein thememory includes computer-readable program instructions that, whenexecuted by the processor, causes the processor to perform a set offunctions comprising: performing a resolution-adaptive fusion of atleast a first three-dimensional (3D) point cloud and a second 3D pointcloud to generate a fused, denoised, and resolution-optimized 3D pointcloud that represents an environment associated with the vehicle,wherein the first 3D point cloud is generated by a first-type 3Dscanning sensor and the second 3D point cloud is generated by asecond-type 3D scanning sensor, wherein the second-type 3D scanningsensor includes a different resolution in each of a plurality ofdifferent measurement dimensions relative to the first-type 3D scanningsensor, and wherein said performing the resolution-adaptive fusionfurther comprises: generating a first volumetric surface function thatincorporates a resolution of the first-type 3D scanning sensor;generating a second volumetric surface functions that incorporates aresolution of the second-type 3D scanning sensor; forming a 3D compositemultiplicative volumetric surface function by multiplying the firstvolumetric surface function for the first-type 3D scanning sensor andthe second volumetric surface function for the second-type 3D scanningsensor; and forming a 3D composite additive volumetric surface functionby adding the first volumetric surface function and the secondvolumetric surface function, wherein the 3D composite additivevolumetric surface function is used to detect the obstacles and the 3Dcomposite multiplicative volumetric surface function is used to refine aresolution of the obstacles; and navigating the vehicle using the fused,denoised, and resolution-optimized 3D point cloud.
 13. The system ofclaim 12, wherein said performing the resolution-adaptive fusioncomprises: generating the first volumetric surface function byperforming a 3D convolution of each measured point from a plurality ofmeasured points of the first 3D point cloud with an associated 3D pointspread function of the first-type 3D scanning sensor for representing anuncertainty in a spatial location of each measured point, wherein thefirst volumetric surface function incorporates a resolution of thefirst-type 3D scanning sensor; and generating the second volumetricsurface function by performing a 3D convolution of each measured pointfrom a plurality of measured points of the second 3D point cloud with anassociated 3D points spread function of the second-type 3D scanningsensor for representing an uncertainty in a spatial location of eachmeasured point, wherein the second volumetric surface functionincorporates a resolution of the second-type 3D scanning sensor.
 14. Thesystem of claim 13, wherein said performing the resolution-adaptivefusion further comprises forming a 3D composite volumetric surfacefunction by multiplying or adding the first volumetric surface functionfor the first-type 3D scanning sensor and the second volumetric surfacefunction for the second-type 3D scanning sensor, wherein inaccuratepoint cloud data from one type 3D scanning sensor will be compensated byaccurate point cloud data from the other type scanning sensor by saidforming the 3D composite volumetric surface function.
 15. The system ofclaim 14, wherein said forming the 3D composite volumetric surfacefunction comprises adding the first volumetric surface function and thesecond volumetric surface function in response to a condition thatcauses one of the 3D scanning sensors to be ineffective.
 16. The systemof claim 14, wherein said forming the 3D composite volumetric surfacefunction comprises multiplying the first volumetric surface function andthe second volumetric surface function to enhance a resolution fordetecting an obstacle in the environment associated with the vehiclecompared to using a volumetric surface function of only one of the 3Dscanning sensors.
 17. The system of claim 14, wherein said performingthe resolution-adaptive fusion further comprises generating aniso-contour of the 3D composite volumetric surface function byperforming automated edge-based thresholding to find a bestresolution-adaptive iso-contour of the 3D composite volumetric surfacefunction, wherein the automated edge-based thresholding is based onvolumetric surface function edge map optimization.
 18. The system ofclaim 17, wherein said performing the automated edge-based thresholdingcomprises increasing a threshold value over a preset range of values todetermine the threshold value that maximizes a number of edges in atwo-dimensional (2D) edge map of the iso-contour of the 3D compositevolumetric surface function.
 19. The system of claim 17, wherein saidperforming the resolution-adaptive fusion further comprises resamplingthe iso-contour of the 3D composite volumetric surface function on auniform grid to form the fused, denoised, and resolution-optimized 3Dpoint cloud.
 20. The method of claim 1, further comprising: generating asequence of fused, denoised, and resolution-optimized 3D point cloudsduring operation of the vehicle; and tracking a moving obstacle fromamong the obstacles using the sequence of fused, denoised, andresolution-optimized 3D point clouds to navigate the vehicle and todetect and avoid the moving obstacle.